581 results about "Fuzz testing" patented technology

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

The invention provides an automatic feedback method based on a protocol fuzz test and related equipment. The method comprises the following steps: acquiring a dynamic configuration file of the protocol fuzz test; performing real-time monitoring on the protocol fuzz test according to the protocol characteristic parameters of the dynamic configuration file, and determining a real-time data stream of the protocol fuzz test; performing multi-level feature analysis on the real-time data stream to judge an abnormal event existing in the protocol fuzz test; generating a feedback instruction corresponding to the abnormal event by performing multi-dimensional correlation analysis based on a knowledge graph on the abnormal event; and sending an exception notification mail to the user terminal according to the feedback instruction. Through an automatic mail notification mechanism, it is ensured that a tester can receive a notification immediately when a test task fails, the workload of manual monitoring is reduced, and the test efficiency is improved.

The invention provides a large-model-assisted program function automatic perception fuzzy test method and system, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring a source code and a description document of a to-be-tested program; analyzing the source code and the description document based on the first large model to obtain all functional parameters supported by the to-be-tested program; identifying a dependency relationship and a constraint condition among the functional parameters based on the source code and the extracted functional parameters; executing a fuzzy test based on the dependency relationship between the function parameters and the constraint condition; and automatically analyzing the test result of the fuzzy test based on the second large model to generate a structured report. According to the method, the coverage rate and effectiveness of fuzzy testing can be optimized, and the quality and safety of a complex software system are comprehensively guaranteed.

The invention discloses a network protocol fuzz testing method and device based on state guidance and seed variation, and the method comprises the steps: 1), manually constructing a high-quality basic seed according to an official RFC protocol document, and enabling the seed to be mapped with a corresponding state; 2) inputting the message sequence into a target program for fuzzy testing, interacting with the target program, monitoring a program state, and obtaining feedback; 3) according to the protocol state code and path coverage rate feedback, selecting seeds with high coverage rate to carry out information retention variation, and reserving the state conversion function of the seeds to reduce the number of invalid seeds; and 4) generating a state guide message sequence: selecting a target test state according to a current state and a returned protocol state code, preferentially selecting a state which is not reached or has few arrival times as a target, and then generating a message sequence capable of reaching or approaching the target state according to the basic seed. According to the method provided by the invention, the state information of the protocol is fully utilized, the state conversion capability of the seed is reserved in the seed variation process, and the efficiency and the quality of the network protocol fuzz test are improved through state guidance and targeted message sequence generation.

The invention relates to a fuzzy test Kubernete-based three-party component vulnerability mining method, which comprises the following steps of: firstly, automatically identifying a user controllable function by combining a large language model based on an official document, a configuration list and source code information of a three-party component, and constructing a keyword dictionary to assist in large-scale identification of a potential target function; then analyzing context information of the target function and an interaction relation of the target function in a Kubernetes cluster, constructing a front dependency library required by fuzzy testing, and automatically generating a fuzzy testing driving program; integrating a fuzzy test driving program with a three-party component source code, performing code instrumentation, pre-compiling and corpus optimization, generating test input according to a set variation strategy, dynamically monitoring a coverage rate and an abnormal behavior, and continuously optimizing a test corpus; and finally, based on the crash behavior of the fuzzy test record, analyzing a trigger path of the vulnerability, performing vulnerability verification in a local Kubernetes cluster environment, and finally generating a vulnerability analysis report.

The invention belongs to the field of fuzz testing, and discloses an industrial control protocol fuzz testing method and system based on a large language model, and the method comprises the steps: obtaining a protocol document, a to-be-tested protocol code, and a protocol sample; analyzing the protocol document based on a large language model to obtain protocol analysis information; performing vulnerability extraction on the protocol sample with the vulnerability information based on a large language model to obtain a plurality of vulnerability documents; based on the retrieval enhancement generation framework, retrieving an external knowledge base by adopting the query statement and the protocol analysis information to generate an enriched initial seed set; performing code auditing on the protocol code to be tested based on the large language model to obtain a vulnerability code of the protocol code, and performing instrumentation on the vulnerability code of the protocol code to obtain an instrumented code; mutation is conducted on the input seeds based on a preset seed energy distribution rule, and fuzzy testing is conducted on the instrumentation codes based on the input seeds subjected to mutation. According to the invention, the probability and speed of generating abnormity in fuzzy testing are improved.

Embodiments of the present disclosure provide a fuzz testing method, an electronic device, and a computer readable storage medium. The fuzz testing method comprises: acquiring a data packet; generating a protocol rule tree model on the basis of the data packet; generating a test case on the basis of the protocol rule tree model; sending the test case to a device under test; and determining a test result on the basis of response information of the device under test.

The invention focuses on the field of fuzz testing, and particularly relates to intelligent auxiliary fuzz testing drive code generation by using a large language model. The invention provides a convenient, fast and automatic fuzz test driving code generation method for Python library code fuzz test by using a large language model and combining a test function and a sample code. The method comprises the steps that Python library source code analysis and drive code sample information extraction are conducted through a static analysis tool, and a data set of a test function and a sample code is obtained; constructing a prompt word engineering structure comprising a drive sample code, a generation task description, a variation strategy introduction, a test function information set, a drive sample code and a code standardization description, and generating zero-sample, less-sample and thinking chain prompts according to the number of samples; building a large language model agent end, transmitting cue words to open-source and closed-source large language models in an HTTP / HTTPS (Hyper Text Transfer Protocol / Hyper Text Transfer Protocol Secure Socket) message mode, and performing reasoning generation of fuzzy test driving codes; after the drive codes are obtained, the fuzz test drive codes which do not pass the pilot run test are generated and screened for multiple rounds, and finally a fuzz test drive code set to be subjected to vulnerability mining is obtained; and performing vulnerability mining on the test function by utilizing the obtained fuzzy test driving code set, and storing a result to a database for analysis.

The invention discloses a fuzzy testing method and device, equipment, a storage medium and a product, and the method comprises the steps: obtaining a target code, carrying out the static analysis of the target code, and determining the possible vulnerability information and vulnerability reachable path information of the target code; generating a test seed according to the possible vulnerability information and the vulnerability reachable path information; performing a fuzzy test on the target code through the test seed, determining vulnerabilities existing in the target code, and collecting dynamic feedback information in the fuzzy test process; and optimizing the test seed according to the dynamic feedback information, and returning to execute the step of performing the fuzzy test on the target code through the test seed until a preset test stop condition is met. According to the fuzzy test method disclosed by the invention, the dynamic feedback information is collected in each fuzzy test process, and the test seeds are optimized according to the dynamic feedback information, so that the test efficiency and accuracy can be improved, and finally efficient vulnerability detection and security evaluation are realized.

The invention relates to the technical field of data processing, and discloses a system kernel fuzzy test seed generation method and system based on a large language model. The method comprises the following steps: performing feature extraction on Linux kernel system calling on a Host host machine to obtain a feature data set; performing CFG analysis on the feature data set to obtain a system call sequence diagram and extracting a state transition table; constructing a test scene for the state transition table, and performing Syzkaller verification to obtain an effective scene library; generating an initial seed case set for the effective scene library, and executing a test in the QEMU to obtain result data; and performing coverage analysis on the result data to obtain a coverage rate report, and optimizing to generate a target test case set. According to the invention, the test coverage rate and the defect discovery capability are improved.

The invention discloses a kernel fuzz testing method and system based on a large language model, and belongs to the technical field of kernel fuzz testing. The method comprises the following steps: constructing a mapping relationship between system calling of a target operating system and a kernel object; based on the mapping relationship between the system call and the kernel object, generating a test case by using a large language model; and under the condition that the test case passes the verification, obtaining a fuzzy test result of the target operating system based on the test case. According to the method, the reasoning generalization ability of the LLM is utilized to generate a large number of effective system call sequences which conform to the dependency relationship and are wide in coverage range, so that the ability of a fuzzy test tool to generate test cases is enhanced, and potential vulnerabilities are mined.

The invention relates to a protocol fuzz testing method and system based on a long-life-cycle enumeration variable state. The method comprises the following steps: S1, a static enumeration declaration statement instrumentation analysis stage; s2, a short life cycle enumeration variable dynamic analysis stage; s3, a static assignment statement instrumentation analysis stage; and S4, a fuzzy test stage. The system comprises a static analysis module, a dynamic analysis module, a network IO module and a variation feedback module. According to the method, the source code of the protocol entity program to be tested is statically analyzed to capture distribution and use of enumeration type variables in the source code. State transition is represented by monitoring changes of enumeration variable value domains, and a corresponding state tree structure is constructed, so that stateful grey box fuzz testing is carried out on any protocol entity program to be tested under the condition that extra manual analysis is not needed.

The invention provides a network protocol fuzz testing method based on strategy gradient reinforcement learning, and relates to the technical field of fuzz testing, and the method comprises the steps: building a test corpus through obtaining a to-be-tested protocol data packet format; establishing an action space containing a mutation operation set and a state space of a benchmark test data packet feature vector; determining a reward value based on the test result; calculating a strategy gradient by using a reward value, and updating strategy network parameters in combination with an adaptive learning rate; and performing fuzzy testing by using the trained model. According to the invention, the test efficiency is improved, the variation strategy selection is optimized, and the effectiveness of the network protocol security test is enhanced.

The present invention provides a fuzz testing system and method based on a virtual computer model. Traditional fuzz testing approaches primarily modify external inputs to detect security vulnerabilities, making it difficult to monitor internal state changes in real-time. This invention constructs a test environment using a virtual computer model and incorporates automatic assertion generation and detection functions, enabling precise analysis of both internal and external signals of the system. After executing fuzz testing, assertion data is analyzed to assess test coverage and identify untested code regions, allowing for an optimized testing strategy. This improves the detection rate of security vulnerabilities, enhances test automation and reliability, and can be applied in various fields such as networks, automotive ECUs, IoT, finance, aerospace, and semiconductors.

The invention discloses a fuzz testing method and system for code generation and automatic program repair based on a large model, which are used for solving the problems of high code generation error rate and high testing cost in the running process of an existing large model fuzzer. The method comprises four stages of cue word screening, fuzzy circulation, program repairing and vulnerability detection. A large language model is used as a main generator, an operator selection problem in a fuzzy loop stage is modeled as a multi-arm tiger machine problem, and a Thompson sampling algorithm is used to improve the diversity and effectiveness of program generation. The generated program is cyclically repaired, so that program errors caused by lack of API (Application Program Interface) combination constraints are reduced. According to the method, the effectiveness of the program generated by the fuzz test based on the large model is obviously improved, the problem that the error rate of the program generated by an existing fuzzer based on the large model is high is solved, and the code coverage rate is higher.

The invention discloses an industrial control protocol intelligent fuzzy test method and system based on a multi-agent large model, and relates to an industrial information security test method and system.According to the industrial control protocol intelligent fuzzy test method and system based on the multi-agent large model, the large model is enhanced by integrating the field of the retrieval enhancement generation (RAG) and the low-rank quantization adaptation (QLoRA) technology, and a multi-agent cooperation mechanism is combined; and full-automatic, high-precision and end-to-end fuzzy testing of the industrial protocol is realized. The system generates seed data meeting protocol specifications through the seed generation agent, the test case generation agent executes field, structure and semantic variation based on the seed data, efficient test cases are dynamically generated, a test strategy is optimized in real time through the feedback analysis and strategy adjustment agent, and the vulnerability discovery capability is improved. By dynamically adjusting the variation strategy and optimizing the generation of the test case, the coverage, the accuracy and the efficiency of the test are improved, and the security and the vulnerability detection capability of the industrial control system are remarkably improved.

The invention provides a distributed system load balancing test method and device based on load difference degree guidance. The method comprises the steps that multi-dimensional input is converted into a one-dimensional initial input operation sequence; wherein the multi-dimensional input comprises one or a combination of more of a working load, a load configuration and a node fault; generating an initial test case according to the initial input operation sequence, and mutating the selected initial test case to obtain a new test case; wherein the initial test case is stored in a seed pool; controlling the distributed system to operate the new test case, and calculating the load difference degree of each pair of nodes in the distributed system according to the load data of the nodes in the distributed system during operation; and determining a target test case according to the load difference degree, and storing the target test case in a seed pool to guide next fuzzy test iteration. According to the method, three-dimensional input of user requests, system configuration changes, system fault conditions and the like which influence a load balancing mechanism is modeled into a standard one-dimensional input sequence, so that subsequent fuzzy testing is facilitated, and the testing efficiency is improved; meanwhile, the load difference degree between the distributed nodes is used as guide feedback to optimize the input space, high-quality test cases can be continuously generated, load imbalance between the nodes in the distributed system is revealed to the maximum extent, developers can be helped to more effectively recognize and solve potential performance bottlenecks, and the test efficiency is improved. The test coverage rate is improved, and the overall stability and response speed of the system are also improved.

The invention discloses a knowledge extraction and envelope coverage-oriented software vulnerability test method and related equipment, and the method comprises the steps: obtaining vulnerability key information of target software, and obtaining sensitive function features through defect dependence analysis and construction based on the vulnerability key information; performing semantic analysis on the analysis report and the code abstract associated with the sensitive function characteristics, screening to obtain a target sensitive function, performing path envelope reverse tracking on the target sensitive function, and constructing to obtain a target path envelope; and obtaining coverage information enveloped by the target path, and carrying out fuzzy testing on the basis of the coverage information in combination with the iteratively optimized variation sample. According to the method, through intelligent closed loop of analysis-positioning-testing-feedback-optimization, static analysis provides accurate guidance for dynamic testing, and the static analysis strategy is inversely optimized by the result of the dynamic testing, so that the maximum improvement of the testing efficiency and the vulnerability discovery accuracy is realized in limited testing resources, and the testing efficiency and the vulnerability discovery accuracy are improved. The method can be widely applied to the technical field of software security.

The invention belongs to the technical field of computer information processing, and particularly discloses an internet of things equipment firmware-oriented fuzz test input optimization device, which comprises a static analysis module used for carrying out firmware analysis through an API interface, identifying an input transmission point of bare computer firmware, and realizing automatic identification through a custom script; the dynamic simulation module is used for identifying an input transfer point of the firmware of the real-time operating system and realizing a length inference mechanism and a final input transfer algorithm; the input routing mapping module is used for identifying and dynamically monitoring a data retrieval point, an input processing point and an availability check instruction in firmware to obtain a mapping result; the input transmission point identification module is used for automatically identifying an input transmission point of the firmware based on the mapping result; and the sensing module is used for dynamically determining the minimum length and the maximum length of the input data of the input transmission point, optimizing the input transmission point of the firmware fuzz test, and dynamically calculating the length of the reserved data. According to the invention, the fuzz testing efficiency can be improved.

The invention discloses an aero-engine compiler fuzzy test method and system based on a large language model. In a variation prompt generation stage, a seed program library is constructed by using embedded programs of various structures and compiler test suite programs, variation operators are selected through diversified guide strategies, and variation prompts are constructed based on a prompt template. In the test generation stage, a large language model is used for generating a variant program with complex data flow and control flow so as to enhance the test coverage rate of a compiler; global and local variables are monitored by inserting test output for the program, so that the program can effectively find out silent miscompilation errors with the maximum harm; in addition, a front-end error feedback strategy is designed, so that the effectiveness of a large language model generation test program is improved. In the differential testing stage, random difference testing and testing under different optimization levels are combined, so that the defects of the middle and rear ends of the compiler are detected. According to the method, the coverage rate of compiler testing can be increased, and silent miscompilation errors can be detected.

The present invention discloses an optimization method for industrial control protocol fuzz testing based on coverage guidance. This method combines mutation testing on the basis of traditional coverage-guided fuzz testing. By comparing the output results of the program under test and the mutated PUT, high-quality test cases are screened. At the same time, the concept of coverage-guided tracking is introduced. By encoding the current coverage boundary in the binary file of the program under test, self-reporting can be performed when new coverage is generated in the test case, eliminating unnecessary tracking of the coverage-guided fuzzer, improving the fuzz testing efficiency, and greatly enhancing the vulnerability detection ability and testing efficiency of coverage-guided fuzz testing.

The invention relates to a protocol fuzz testing method and system based on multi-dimensional feedback information, and the method comprises the steps: carrying out the preprocessing of a target protocol entity program, and carrying out the testing preparation; performing a fuzzy test; the fuzzy testing process comprises a selection field variation stage and a variation field cost probability calculation stage for multi-dimensional feedback information processing; obtaining test result information, and feeding back the result information. The system comprises a test preparation module, a fuzzy test module and a result information feedback module. Multi-dimensional feedback information processing is carried out through the selection field variation stage and the variation field cost probability calculation stage, code space coverage of protocol fuzz testing is remarkably improved, more potential vulnerabilities are revealed, testing efficiency is greatly improved, under limited resources, testing is concentrated on a path which is most likely to cause collapse, and the testing efficiency is greatly improved. Therefore, the detection quality and speed are improved.

The invention relates to the technical field of software security detection, and particularly discloses a network protocol fuzz test system based on enhanced context information, comprising: an enhanced context information module used for acquiring state information and seed information, and performing instrumentation on enhanced context information through a track adjacent Call instruction; the state selection module is used for selecting a target state for fuzzy testing according to a LinUCB selection algorithm, and the LinUCB selection algorithm obtains the target state based on the context vector of the state and the reward; the seed selection module is used for selecting a target seed for fuzzy testing according to a ternary seed selection algorithm; according to the ternary seed selection algorithm, a seed label, execution information and session information are extracted from each seed in a seed pool to calculate a seed value, and then a target seed is obtained according to the seed value. According to the invention, the coverage rate of fuzzy testing can be improved.

The invention relates to the technical field of Internet of Things equipment security detection, in particular to an Internet of Things equipment vulnerability detection method and system based on sensitive target guidance, and the method comprises the steps: extracting firmware key information of to-be-detected equipment; obtaining potential sensitive targets and paths in firmware of the to-be-detected equipment and basic block positions corresponding to the sensitive targets and the paths through reachable data streams in decompilation code execution; performing pruning operation on the control flow diagram in the key information according to the positions of the basic blocks corresponding to the sensitive target and the path, and obtaining a test space related to the sensitive target and the path; variation is carried out on request processing information, parameter information and control information in the test space, and the test case is guided to be executed to a sensitive target; and performing collapse variation on the sensitive function parameters in the test space, triggering the firmware vulnerability of the to-be-detected equipment in the execution process of the test case, and generating a test result. According to the method, the blindness of fuzzy testing is reduced, and the method has good adaptability to firmware analysis of embedded equipment.

The invention discloses a kernel boot test method for enhancing startability and stability by using Ranconfig for an edge device, which comprises the following steps of: generating a random kernel configuration file by using a Ranconfig tool, carrying out compiling and startup test on the generated configuration file, repairing a non-startable configuration file by using a config-bias algorithm, and carrying out kernel boot test on the kernel configuration file by using the Ranconfig tool. Generating a configuration item difference set according to the difference between the minimum default configuration file and the to-be-repaired configuration file, iteratively positioning configuration items failed to start and recording the configuration items to a key configuration database; the repaired kernel is further subjected to a pressure test through a fuzzy test module, and a black list and a white list are generated in combination with equipment hardware resources, so that configuration items are optimized, and the test coverage rate is increased; according to the invention, the availability and the stability of the kernel boot test of the edge device are improved.

The invention discloses an IKE (Internet Key Exchange) protocol fuzzy testing method based on state guidance, which is characterized in that an IKE session is established and interacted with a target system based on an active learning algorithm in model learning, and a state learning module deduces a state machine model when the target system realizes an IKE protocol in a member query and equivalent query mode; and meanwhile, the fuzzy test module takes the deduced state machine model as guidance, extracts a preorder guidance message sequence and a tested message of each state, carries out a black box-based fuzzy test on each state, and mines deep program vulnerabilities related to the IKE protocol.

The invention discloses a stateful protocol fuzz testing method and system based on a large model construction state machine, and the method comprises the steps: firstly preprocessing a protocol specification document, guiding an information large model to analyze through cue words to obtain state machine information, constructing an initial state machine through the state machine large model, and initializing a message dictionary; secondly, protocol testing is carried out based on an initial state machine, a prompt word is input into a large testing model to understand a flow log, a message field supplement and improvement dictionary is obtained, message request fields and response codes in the message dictionary are combined, the initial state machine is improved, and a fine-grained state machine is obtained; and finally, on the basis of a fine-grained state machine, inputting cue words, analyzing a blind area covered by the current test by an analysis large model, and guiding a fuzzy tester to preferentially test an uncovered state which is most likely to trigger the deep vulnerability, thereby completing the fuzzy test. According to the method, the fuzzy device directionally tests the potential test state area, and accurate and efficient fuzzy testing is carried out.

The invention belongs to the field of universal software fuzz testing, and discloses a dynamic clustering fuzz testing method and system based on efficient byte exploration. According to the method, fuzzy test seed bytes are subjected to clustering analysis by using a density-based spatial clustering algorithm, the positions of key bytes are identified, and the effective variation times of the key bytes are recorded. Then, corresponding weights are distributed to the bytes according to the clustering result and the number of effective variation times of the clustering result, an energy distribution strategy is dynamically adjusted in combination with the path coverage rate and path novelty information, and the exploration efficiency of a new path is improved. And performing mutation on the high-weight bytes to generate effective seeds of a new path, locking key byte positions of the effective seeds, performing normalization processing on other positions, and continuing mutation operation. The test strategy is continuously optimized through repeated execution of the steps, and the fuzzy test efficiency and the vulnerability discovery capability are effectively improved.

The invention discloses a Restful API fuzzy test method and system based on tree structure generation parameters, and the method comprises the steps: obtaining Restful API document data, carrying out the analysis and recognition processing, and generating a Restful API request sequence; mapping processing is carried out on request parameters in the Restful API request sequence, and a parameter tree is constructed; performing parameter value calculation on nodes in the parameter tree through a parameter value generation function to obtain parameter values of the nodes of the parameter tree; performing parameter structure variation based on the parameter tree node parameter values to obtain a parameter tree with rich parameter variants; and requesting and verifying the parameter tree with rich parameter variants, outputting a vulnerability report, and completing Restful API fuzz testing. According to the Restful API fuzz testing method, a complex parameter structure can be efficiently processed, so that comprehensive and efficient Restful API fuzz testing is realized. The Restful API fuzzy testing method and system based on the tree structure generation parameters can be widely applied to the technical field of Web security.